As network communications among multiple computing devices have become ubiquitous, the quantity of information available via such network communications has increased exponentially. For example, the ubiquitous Internet and World Wide Web comprise information sourced by a vast array of entities throughout the world, including corporations, universities, individuals and the like. Such information is often marked, or “tagged”, in such a manner that it can be found, identified and indexed by services known as “search engines”. Even information that is not optimized for search engine indexing can still be located by services, associated with search engines, which seek out information available through network communications with other computing devices and enable a search engine to index such information for subsequent retrieval.
Due to the sheer volume of information that is available to computing devices through such network communicational connections with other computing devices, users increasingly turn to search engines to find the information they seek. Search engines typically enable users to search for any topic and retrieve, from this vast volume of information, identifications of specific content that is deemed to be responsive to, or associated with, the users' queries. To sort through the vast amounts of information that is available, and timely provide useful responses to users' queries, search engines employ a myriad of mechanisms to optimize the identification and retrieval of responsive and associated information.
One mechanism employed by search engines to increase the chances of providing relevant content in response to users' queries is to collect contextual information from the users submitting the queries. For example, a search engine can utilize the immediately preceding search queries submitted by a user to gain a better understanding of what the user is looking for, or to more accurately glean the user's intentions. A user model can also be built from other user-centric data that the user provides access to, such as their social network information, their computing device information and the like. Search engines also enable users to establish user identities through which users can explicitly indicate specific preferences, attributes and other like information. For example, users can specify a level of filtering to be applied to avoid receiving potentially offensive content. As another example, users can specify geographic location preferences such that searches for physical entities can be limited to particular geographic regions that are relevant to the user.
The ubiquitous network communication connections between computing devices also enable users to interoperate with one another more efficiently than previously possible. One such interoperation of users is in the context of searching, where multiple users can perform a collaborative search. A search engine, upon receiving a search query of a collaborative search, can seek to identify responsive information that is tailored to the multiple users performing the collaborative search. For example, if one user performing a collaborative search had specified a particular geographic region in their user profile, and another user in that same collaborative search had specified a different geographic region in their user profile, the search engine could seek to identify responsive information associated with the intersection of the geographic areas specified by the users performing the collaborative search.